forked from KEMT/zpwiki
247 lines
9.2 KiB
Python
247 lines
9.2 KiB
Python
import numpy as np
|
|
import torch
|
|
import torch.autograd as autograd
|
|
import torch.nn as nn
|
|
import torch.optim as optim
|
|
from sklearn import metrics
|
|
from datetime import datetime
|
|
|
|
torch.manual_seed(1)
|
|
|
|
def argmax(vec):
|
|
# return the argmax as a python int
|
|
_, idx = torch.max(vec, 1)
|
|
return idx.item()
|
|
|
|
|
|
def prepare_sequence(seq, to_ix):
|
|
idxs = [to_ix[w] for w in seq]
|
|
return torch.tensor(idxs, dtype=torch.long)
|
|
|
|
|
|
# Compute log sum exp in a numerically stable way for the forward algorithm
|
|
def log_sum_exp(vec):
|
|
max_score = vec[0, argmax(vec)]
|
|
max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])
|
|
return max_score + \
|
|
torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))
|
|
|
|
class BiLSTM_CRF(nn.Module):
|
|
|
|
def __init__(self, vocab_size, tag_to_ix, embedding_dim, hidden_dim):
|
|
super(BiLSTM_CRF, self).__init__()
|
|
self.embedding_dim = embedding_dim
|
|
self.hidden_dim = hidden_dim
|
|
self.vocab_size = vocab_size
|
|
self.tag_to_ix = tag_to_ix
|
|
self.tagset_size = len(tag_to_ix)
|
|
|
|
self.word_embeds = nn.Embedding(vocab_size, embedding_dim)
|
|
self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2,
|
|
num_layers=1, bidirectional=True)
|
|
|
|
# Maps the output of the LSTM into tag space.
|
|
self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)
|
|
|
|
# Matrix of transition parameters. Entry i,j is the score of
|
|
# transitioning *to* i *from* j.
|
|
self.transitions = nn.Parameter(
|
|
torch.randn(self.tagset_size, self.tagset_size))
|
|
|
|
# These two statements enforce the constraint that we never transfer
|
|
# to the start tag and we never transfer from the stop tag
|
|
self.transitions.data[tag_to_ix[START_TAG], :] = -10000
|
|
self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000
|
|
|
|
self.hidden = self.init_hidden()
|
|
|
|
def init_hidden(self):
|
|
return (torch.randn(2, 1, self.hidden_dim // 2),
|
|
torch.randn(2, 1, self.hidden_dim // 2))
|
|
|
|
def _forward_alg(self, feats):
|
|
# Do the forward algorithm to compute the partition function
|
|
init_alphas = torch.full((1, self.tagset_size), -10000.)
|
|
# START_TAG has all of the score.
|
|
init_alphas[0][self.tag_to_ix[START_TAG]] = 0.
|
|
|
|
# Wrap in a variable so that we will get automatic backprop
|
|
forward_var = init_alphas
|
|
|
|
# Iterate through the sentence
|
|
for feat in feats:
|
|
alphas_t = [] # The forward tensors at this timestep
|
|
for next_tag in range(self.tagset_size):
|
|
# broadcast the emission score: it is the same regardless of
|
|
# the previous tag
|
|
emit_score = feat[next_tag].view(
|
|
1, -1).expand(1, self.tagset_size)
|
|
# the ith entry of trans_score is the score of transitioning to
|
|
# next_tag from i
|
|
trans_score = self.transitions[next_tag].view(1, -1)
|
|
# The ith entry of next_tag_var is the value for the
|
|
# edge (i -> next_tag) before we do log-sum-exp
|
|
next_tag_var = forward_var + trans_score + emit_score
|
|
# The forward variable for this tag is log-sum-exp of all the
|
|
# scores.
|
|
alphas_t.append(log_sum_exp(next_tag_var).view(1))
|
|
forward_var = torch.cat(alphas_t).view(1, -1)
|
|
terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
|
|
alpha = log_sum_exp(terminal_var)
|
|
return alpha
|
|
|
|
def _get_lstm_features(self, sentence):
|
|
self.hidden = self.init_hidden()
|
|
embeds = self.word_embeds(sentence).view(len(sentence), 1, -1)
|
|
lstm_out, self.hidden = self.lstm(embeds, self.hidden)
|
|
lstm_out = lstm_out.view(len(sentence), self.hidden_dim)
|
|
lstm_feats = self.hidden2tag(lstm_out)
|
|
return lstm_feats
|
|
|
|
def _score_sentence(self, feats, tags):
|
|
# Gives the score of a provided tag sequence
|
|
score = torch.zeros(1)
|
|
tags = torch.cat([torch.tensor([self.tag_to_ix[START_TAG]], dtype=torch.long), tags])
|
|
for i, feat in enumerate(feats):
|
|
score = score + \
|
|
self.transitions[tags[i + 1], tags[i]] + feat[tags[i + 1]]
|
|
score = score + self.transitions[self.tag_to_ix[STOP_TAG], tags[-1]]
|
|
return score
|
|
|
|
def _viterbi_decode(self, feats):
|
|
backpointers = []
|
|
|
|
# Initialize the viterbi variables in log space
|
|
init_vvars = torch.full((1, self.tagset_size), -10000.)
|
|
init_vvars[0][self.tag_to_ix[START_TAG]] = 0
|
|
|
|
# forward_var at step i holds the viterbi variables for step i-1
|
|
forward_var = init_vvars
|
|
for feat in feats:
|
|
bptrs_t = [] # holds the backpointers for this step
|
|
viterbivars_t = [] # holds the viterbi variables for this step
|
|
|
|
for next_tag in range(self.tagset_size):
|
|
# next_tag_var[i] holds the viterbi variable for tag i at the
|
|
# previous step, plus the score of transitioning
|
|
# from tag i to next_tag.
|
|
# We don't include the emission scores here because the max
|
|
# does not depend on them (we add them in below)
|
|
next_tag_var = forward_var + self.transitions[next_tag]
|
|
best_tag_id = argmax(next_tag_var)
|
|
bptrs_t.append(best_tag_id)
|
|
viterbivars_t.append(next_tag_var[0][best_tag_id].view(1))
|
|
# Now add in the emission scores, and assign forward_var to the set
|
|
# of viterbi variables we just computed
|
|
forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)
|
|
backpointers.append(bptrs_t)
|
|
|
|
# Transition to STOP_TAG
|
|
terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
|
|
best_tag_id = argmax(terminal_var)
|
|
path_score = terminal_var[0][best_tag_id]
|
|
|
|
# Follow the back pointers to decode the best path.
|
|
best_path = [best_tag_id]
|
|
for bptrs_t in reversed(backpointers):
|
|
best_tag_id = bptrs_t[best_tag_id]
|
|
best_path.append(best_tag_id)
|
|
# Pop off the start tag (we dont want to return that to the caller)
|
|
start = best_path.pop()
|
|
assert start == self.tag_to_ix[START_TAG] # Sanity check
|
|
best_path.reverse()
|
|
return path_score, best_path
|
|
|
|
def neg_log_likelihood(self, sentence, tags):
|
|
feats = self._get_lstm_features(sentence)
|
|
forward_score = self._forward_alg(feats)
|
|
gold_score = self._score_sentence(feats, tags)
|
|
return forward_score - gold_score
|
|
|
|
def forward(self, sentence): # dont confuse this with _forward_alg above.
|
|
# Get the emission scores from the BiLSTM
|
|
lstm_feats = self._get_lstm_features(sentence)
|
|
|
|
# Find the best path, given the features.
|
|
score, tag_seq = self._viterbi_decode(lstm_feats)
|
|
return score, tag_seq
|
|
|
|
START_TAG = "<START>"
|
|
STOP_TAG = "<STOP>"
|
|
EMBEDDING_DIM = 5
|
|
HIDDEN_DIM = 4
|
|
|
|
# Make up some training data
|
|
with open('/home/dlindvai/work/text.txt', 'r') as text2:
|
|
with open('/home/dlindvai/work/tags.txt', 'r') as tags2:
|
|
text1 = text2.read().splitlines()
|
|
tags1 = tags2.read().splitlines()
|
|
|
|
for line in text1:
|
|
text = line.replace("['", "").replace("']", "")
|
|
for line in tags1:
|
|
tags = line.replace("['", "").replace("']", "")
|
|
|
|
training_data = [( text.split() , tags.split() )]
|
|
#print(training_data)
|
|
|
|
word_to_ix = {}
|
|
for sentence, tags in training_data:
|
|
for word in sentence:
|
|
if word not in word_to_ix:
|
|
word_to_ix[word] = len(word_to_ix)
|
|
|
|
tag_to_ix = {"S": 0, "P": 1, "C": 2, "Q": 3, "N": 4, START_TAG: 5, STOP_TAG: 6}
|
|
|
|
model = BiLSTM_CRF(len(word_to_ix), tag_to_ix, EMBEDDING_DIM, HIDDEN_DIM)
|
|
optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4)
|
|
|
|
|
|
# Check predictions before training
|
|
with torch.no_grad():
|
|
precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)
|
|
precheck_tags = torch.tensor([tag_to_ix[t] for t in training_data[0][1]], dtype=torch.long)
|
|
#print(model(precheck_sent))
|
|
|
|
|
|
# Print start time
|
|
start = datetime.now()
|
|
start_time = start.strftime("%H:%M:%S")
|
|
print("Start time = ", start_time)
|
|
|
|
for epoch in range(50):
|
|
for sentence, tags in training_data:
|
|
# Step 1. Remember that Pytorch accumulates gradients. We need to clear them out before each instance
|
|
model.zero_grad()
|
|
|
|
# Step 2. Get our inputs ready for the network, that is, turn them into Tensors of word indices.
|
|
sentence_in = prepare_sequence(sentence, word_to_ix)
|
|
targets = torch.tensor([tag_to_ix[t] for t in tags], dtype=torch.long)
|
|
|
|
# Step 3. Run our forward pass.
|
|
loss = model.neg_log_likelihood(sentence_in, targets)
|
|
|
|
# Step 4. Compute the loss, gradients, and update the parameters by calling optimizer.step()
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
|
|
# Check predictions after training
|
|
with torch.no_grad():
|
|
precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)
|
|
#print(model(precheck_sent))
|
|
|
|
|
|
# Error calculator
|
|
var = model(precheck_sent)
|
|
y_true = np.array(targets)
|
|
y_pred = np.array(var[1])
|
|
|
|
print(metrics.confusion_matrix(y_true, y_pred))
|
|
print(metrics.classification_report(y_true, y_pred, digits=3))
|
|
|
|
# Print finish time
|
|
finish = datetime.now()
|
|
finish_time = finish.strftime("%H:%M:%S")
|
|
print("Finish time = ", finish_time)
|